Modeling of Fatigue Wear of Viscoelastic Coatings

A new model for studying the kinetics of fatigue wear of a viscoelastic coating bonded to a rigid substrate is proposed. The fatigue mechanism is due to the cyclic interaction of the coating with a rough counterbody, which is modeled by a periodic system of smooth indenters. The study includes the solution of the problem of sliding contact of the indenter at a constant velocity along the viscoelastic coating, the calculation of stresses taking into account the mutual effect, and study of the process of damage accumulation in the material. The calculation of the damage function of the surface layer was carried out using the reduced stress criterion. Assuming the possibility of summation of accumulated damage, two processes were considered: delamination of surface layers of the coating and continuous fracture of the surface by the fatigue mechanism. The effect of the sliding velocity and viscoelastic properties of the material on the damage accumulation and the coating wear rate was analyzed. Two types of load, constant and stochastically varying, were used in modeling and analysis. It was found that the rate of fatigue wear of the coating increased and then became constant.

Effects of Notches and Defects on Dwell Fatigue Mechanism and Fatigue Life of Ti-6Al-4V ELI Alloy Used in Deep-Sea Submersibles

The notch (i.e., stress concentration) and defect are important factors influencing the conventional fatigue behavior of metallic materials. What is the influence of notches and defects on the dwell fatigue mechanism and fatigue life? In this paper, the effects of notches and defects on the dwell fatigue behavior of the Ti-6Al-4V ELI alloy used in deep-sea submersibles are investigated under the load control mode. It is shown that the dwell fatigue is insensitive to the defect size (190–438 μm) compared to the conventional fatigue. For notched specimens, they all present fatigue failure mode under dwell fatigue testing, and the dwell fatigue life is higher than that of the smooth specimen at the same local maximum stress. The dwell of the maximum stress has no influence on the fatigue life and failure mechanism for notched specimens. Moreover, the facet feature is observed in the crack initiation region for both the conventional and dwell fatigue of notched specimens. Electron backscatter diffraction observation indicates that the feature of the fine line markings on the facet in the image by scanning electron microscope is due to the steps on the fracture surface of the α grain.

A Multivariate Weighted Ordinal Pattern Transition Network for Characterizing Driver Fatigue Behavior from EEG Signals

Driver fatigue has caused numerous vehicle crashes and traffic injuries. Exploring the fatigue mechanism and detecting fatigue state are of great significance for preventing traffic accidents, and further lessening economic and societal loss. Due to the objectivity of EEG signals and the availability of EEG acquisition equipment, EEG-based fatigue detection task has raised great attention in recent years. Although there exist various methods for this task, the study of fatigue mechanism and detection of fatigue state still remain much to be explored. To investigate these problems, a multivariate weighted ordinal pattern transition (MWOPT) network is proposed in this paper. To be specific, a simulated driving experiment was first conducted to obtain the EEG signals of subjects in alert state and fatigue state respectively. Then the MWOPT network is constructed based on a novel Shannon entropy. To probe into the mechanism underlying fatigue behavior, the small-worldness index is extracted from the generated MWOPT network. Furthermore, the nodal degree index is input into a classifier to distinguish the fatigue state from alert state. The obtained high accuracy indicates the effectiveness of the proposed network for EEG-based fatigue detection. Besides, four nodes are found to play an important role in identifying fatigue state. These results suggest that the proposed method enables to analyze nonlinear multivariate time series and investigate the driving fatigue behavior.